# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
from math import sqrt

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
# from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from einops import rearrange


class FocusedLinearAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size=[20, 20], num_heads=8, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.,
                 focusing_factor=3, kernel_size=5):

        super().__init__()
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads

        self.focusing_factor = focusing_factor
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.softmax = nn.Softmax(dim=-1)

        self.dwc = nn.Conv2d(in_channels=head_dim, out_channels=head_dim, kernel_size=kernel_size,
                             groups=head_dim, padding=kernel_size // 2)
        self.scale = nn.Parameter(torch.zeros(size=(1, 1, dim)))
        self.positional_encoding = nn.Parameter(torch.zeros(size=(1, window_size[0] * window_size[1], dim)))
        print('Linear Attention window{} f{} kernel{}'.
              format(window_size, focusing_factor, kernel_size))

    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        print(x.shape)   # torch.Size([4, 256, 16, 21])   16*21 = 336 != 324   报错
        x = x.flatten(2).transpose(1, 2)
        # print(x.shape)

        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, C).permute(2, 0, 1, 3)
        q, k, v = qkv.unbind(0)
        k = k + self.positional_encoding[:, :k.shape[1], :]
        focusing_factor = self.focusing_factor
        kernel_function = nn.ReLU()
        q = kernel_function(q) + 1e-6
        k = kernel_function(k) + 1e-6
        scale = nn.Softplus()(self.scale)
        q = q / scale
        k = k / scale
        q_norm = q.norm(dim=-1, keepdim=True)
        k_norm = k.norm(dim=-1, keepdim=True)
        if float(focusing_factor) <= 6:
            q = q ** focusing_factor
            k = k ** focusing_factor
        else:
            q = (q / q.max(dim=-1, keepdim=True)[0]) ** focusing_factor
            k = (k / k.max(dim=-1, keepdim=True)[0]) ** focusing_factor
        q = (q / q.norm(dim=-1, keepdim=True)) * q_norm
        k = (k / k.norm(dim=-1, keepdim=True)) * k_norm
        # print(x.shape)
        q, k, v = (rearrange(x, "b n (h c) -> (b h) n c", h=self.num_heads) for x in [q, k, v])
        i, j, c, d = q.shape[-2], k.shape[-2], k.shape[-1], v.shape[-1]

        z = 1 / (torch.einsum("b i c, b c -> b i", q, k.sum(dim=1)) + 1e-6)
        if i * j * (c + d) > c * d * (i + j):
            kv = torch.einsum("b j c, b j d -> b c d", k, v)
            x = torch.einsum("b i c, b c d, b i -> b i d", q, kv, z)
        else:
            qk = torch.einsum("b i c, b j c -> b i j", q, k)
            x = torch.einsum("b i j, b j d, b i -> b i d", qk, v, z)

        num = int(v.shape[1] ** 0.5)
        # num = sqrt(v.shape[1])
        # print(v.shape)
        feature_map = rearrange(v, "b (w h) c -> b c w h", w=num, h=num)
        feature_map = rearrange(self.dwc(feature_map), "b c w h -> b (w h) c")
        x = x + feature_map

        x = rearrange(x, "(b h) n c -> b n (h c)", h=self.num_heads)
        x = self.proj(x)
        x = self.proj_drop(x)
        x = rearrange(x, "b (w h) c -> b c w h", b=B, c=self.dim, w=num, h=num)
        return x

    def eval(self):
        super().eval()
        print('eval')

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'


# class Mlp(nn.Module):
#     def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
#         super().__init__()
#         out_features = out_features or in_features
#         hidden_features = hidden_features or in_features
#         self.fc1 = nn.Linear(in_features, hidden_features)
#         self.act = act_layer()
#         self.fc2 = nn.Linear(hidden_features, out_features)
#         self.drop = nn.Dropout(drop)
#
#     def forward(self, x):
#         x = self.fc1(x)
#         x = self.act(x)
#         x = self.drop(x)
#         x = self.fc2(x)
#         x = self.drop(x)
#         return x
#
#
# def window_partition(x, window_size):
#     """
#     Args:
#         x: (B, H, W, C)
#         window_size (int): window size
#
#     Returns:
#         windows: (num_windows*B, window_size, window_size, C)
#     """
#     B, H, W, C = x.shape
#     x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
#     windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
#     return windows
#
#
# def window_reverse(windows, window_size, H, W):
#     """
#     Args:
#         windows: (num_windows*B, window_size, window_size, C)
#         window_size (int): Window size
#         H (int): Height of image
#         W (int): Width of image
#
#     Returns:
#         x: (B, H, W, C)
#     """
#     B = int(windows.shape[0] / (H * W / window_size / window_size))
#     x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
#     x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
#     return x


# class WindowAttention(nn.Module):
#     r""" Window based multi-head self attention (W-MSA) module with relative position bias.
#     It supports both of shifted and non-shifted window.
#
#     Args:
#         dim (int): Number of input channels.
#         window_size (tuple[int]): The height and width of the window.
#         num_heads (int): Number of attention heads.
#         qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
#         qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
#         attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
#         proj_drop (float, optional): Dropout ratio of output. Default: 0.0
#     """
#
#     def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
#
#         super().__init__()
#         self.dim = dim
#         self.window_size = window_size  # Wh, Ww
#         self.num_heads = num_heads
#         head_dim = dim // num_heads
#         self.scale = qk_scale or head_dim ** -0.5
#
#         # define a parameter table of relative position bias
#         self.relative_position_bias_table = nn.Parameter(
#             torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
#
#         # get pair-wise relative position index for each token inside the window
#         coords_h = torch.arange(self.window_size[0])
#         coords_w = torch.arange(self.window_size[1])
#         coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
#         coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
#         relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
#         relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
#         relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
#         relative_coords[:, :, 1] += self.window_size[1] - 1
#         relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
#         relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
#         self.register_buffer("relative_position_index", relative_position_index)
#
#         self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
#         self.attn_drop = nn.Dropout(attn_drop)
#         self.proj = nn.Linear(dim, dim)
#         self.proj_drop = nn.Dropout(proj_drop)
#
#         trunc_normal_(self.relative_position_bias_table, std=.02)
#         self.softmax = nn.Softmax(dim=-1)
#
#     def forward(self, x, mask=None):
#         """
#         Args:
#             x: input features with shape of (num_windows*B, N, C)
#             mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
#         """
#         B_, N, C = x.shape
#         qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
#         q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
#
#         q = q * self.scale
#         attn = (q @ k.transpose(-2, -1))
#
#         relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
#             self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
#         relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
#         attn = attn + relative_position_bias.unsqueeze(0)
#
#         if mask is not None:
#             nW = mask.shape[0]
#             attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
#             attn = attn.view(-1, self.num_heads, N, N)
#             attn = self.softmax(attn)
#         else:
#             attn = self.softmax(attn)
#
#         attn = self.attn_drop(attn)
#
#         x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
#         x = self.proj(x)
#         x = self.proj_drop(x)
#         return x
#
#     def extra_repr(self) -> str:
#         return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
#
#     def flops(self, N):
#         # calculate flops for 1 window with token length of N
#         flops = 0
#         # qkv = self.qkv(x)
#         flops += N * self.dim * 3 * self.dim
#         # attn = (q @ k.transpose(-2, -1))
#         flops += self.num_heads * N * (self.dim // self.num_heads) * N
#         #  x = (attn @ v)
#         flops += self.num_heads * N * N * (self.dim // self.num_heads)
#         # x = self.proj(x)
#         flops += N * self.dim * self.dim
#         return flops


# class SwinTransformerBlock(nn.Module):
#     r""" Swin Transformer Block.
#
#     Args:
#         dim (int): Number of input channels.
#         input_resolution (tuple[int]): Input resulotion.
#         num_heads (int): Number of attention heads.
#         window_size (int): Window size.
#         shift_size (int): Shift size for SW-MSA.
#         mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
#         qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
#         qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
#         drop (float, optional): Dropout rate. Default: 0.0
#         attn_drop (float, optional): Attention dropout rate. Default: 0.0
#         drop_path (float, optional): Stochastic depth rate. Default: 0.0
#         act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
#         norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
#     """
#
#     def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
#                  mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
#                  act_layer=nn.GELU, norm_layer=nn.LayerNorm,
#                  focusing_factor=3, kernel_size=5, attn_type='L'):
#         super().__init__()
#         self.dim = dim
#         self.input_resolution = input_resolution
#         self.num_heads = num_heads
#         self.window_size = window_size
#         self.shift_size = shift_size
#         self.mlp_ratio = mlp_ratio
#         if min(self.input_resolution) <= self.window_size:
#             # if window size is larger than input resolution, we don't partition windows
#             self.shift_size = 0
#             self.window_size = min(self.input_resolution)
#         assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
#
#         self.norm1 = norm_layer(dim)
#         assert attn_type in ['L', 'S']
#         if attn_type == 'L':
#             self.attn = FocusedLinearAttention(
#                 dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
#                 qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
#                 focusing_factor=focusing_factor, kernel_size=kernel_size)
#         else:
#             self.attn = WindowAttention(
#                 dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
#                 qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
#
#         self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
#         self.norm2 = norm_layer(dim)
#         mlp_hidden_dim = int(dim * mlp_ratio)
#         self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
#
#         if self.shift_size > 0:
#             # calculate attention mask for SW-MSA
#             H, W = self.input_resolution
#             img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
#             h_slices = (slice(0, -self.window_size),
#                         slice(-self.window_size, -self.shift_size),
#                         slice(-self.shift_size, None))
#             w_slices = (slice(0, -self.window_size),
#                         slice(-self.window_size, -self.shift_size),
#                         slice(-self.shift_size, None))
#             cnt = 0
#             for h in h_slices:
#                 for w in w_slices:
#                     img_mask[:, h, w, :] = cnt
#                     cnt += 1
#
#             mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
#             mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
#             attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
#             attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
#         else:
#             attn_mask = None
#
#         self.register_buffer("attn_mask", attn_mask)
#
#     def forward(self, x):
#         H, W = self.input_resolution
#         B, L, C = x.shape
#         assert L == H * W, "input feature has wrong size"
#
#         shortcut = x
#         x = self.norm1(x)
#         x = x.view(B, H, W, C)
#
#         # cyclic shift
#         if self.shift_size > 0:
#             shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
#         else:
#             shifted_x = x
#
#         # partition windows
#         x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
#         x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
#
#         # W-MSA/SW-MSA
#         attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
#
#         # merge windows
#         attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
#         shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
#
#         # reverse cyclic shift
#         if self.shift_size > 0:
#             x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
#         else:
#             x = shifted_x
#         x = x.view(B, H * W, C)
#
#         # FFN
#         x = shortcut + self.drop_path(x)
#         x = x + self.drop_path(self.mlp(self.norm2(x)))
#
#         return x
#
#     def extra_repr(self) -> str:
#         return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
#                f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
#
#     def flops(self):
#         flops = 0
#         H, W = self.input_resolution
#         # norm1
#         flops += self.dim * H * W
#         # W-MSA/SW-MSA
#         nW = H * W / self.window_size / self.window_size
#         flops += nW * self.attn.flops(self.window_size * self.window_size)
#         # mlp
#         flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
#         # norm2
#         flops += self.dim * H * W
#         return flops


# class PatchMerging(nn.Module):
#     r""" Patch Merging Layer.
#
#     Args:
#         input_resolution (tuple[int]): Resolution of input feature.
#         dim (int): Number of input channels.
#         norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
#     """
#
#     def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
#         super().__init__()
#         self.input_resolution = input_resolution
#         self.dim = dim
#         self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
#         self.norm = norm_layer(4 * dim)
#
#     def forward(self, x):
#         """
#         x: B, H*W, C
#         """
#         H, W = self.input_resolution
#         B, L, C = x.shape
#         assert L == H * W, "input feature has wrong size"
#         assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
#
#         x = x.view(B, H, W, C)
#
#         x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
#         x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
#         x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
#         x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
#         x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
#         x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
#
#         x = self.norm(x)
#         x = self.reduction(x)
#
#         return x
#
#     def extra_repr(self) -> str:
#         return f"input_resolution={self.input_resolution}, dim={self.dim}"
#
#     def flops(self):
#         H, W = self.input_resolution
#         flops = H * W * self.dim
#         flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
#         return flops


# class BasicLayer(nn.Module):
#     """ A basic Swin Transformer layer for one stage.
#
#     Args:
#         dim (int): Number of input channels.
#         input_resolution (tuple[int]): Input resolution.
#         depth (int): Number of blocks.
#         num_heads (int): Number of attention heads.
#         window_size (int): Local window size.
#         mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
#         qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
#         qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
#         drop (float, optional): Dropout rate. Default: 0.0
#         attn_drop (float, optional): Attention dropout rate. Default: 0.0
#         drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
#         norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
#         downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
#         use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
#     """
#
#     def __init__(self, dim, input_resolution, depth, num_heads, window_size,
#                  mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
#                  drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
#                  focusing_factor=3, kernel_size=5, attn_type='L'):
#
#         super().__init__()
#         self.dim = dim
#         self.input_resolution = input_resolution
#         self.depth = depth
#         self.use_checkpoint = use_checkpoint
#
#         # build blocks
#         attn_types = [(attn_type if attn_type[0] != 'M' else ('L' if i < int(attn_type[1:]) else 'S')) for i in range(depth)]
#         window_sizes = [(window_size if attn_types[i] == 'L' else (7 if window_size <= 56 else 12)) for i in range(depth)]
#         self.blocks = nn.ModuleList([
#             SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
#                                  num_heads=num_heads, window_size=window_sizes[i],
#                                  shift_size=0 if (i % 2 == 0) else window_sizes[i] // 2,
#                                  mlp_ratio=mlp_ratio,
#                                  qkv_bias=qkv_bias, qk_scale=qk_scale,
#                                  drop=drop, attn_drop=attn_drop,
#                                  drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
#                                  norm_layer=norm_layer,
#                                  focusing_factor=focusing_factor,
#                                  kernel_size=kernel_size,
#                                  attn_type=attn_types[i])
#             for i in range(depth)])
#
#         # patch merging layer
#         if downsample is not None:
#             self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
#         else:
#             self.downsample = None
#
#     def forward(self, x):
#         for blk in self.blocks:
#             if self.use_checkpoint:
#                 x = checkpoint.checkpoint(blk, x)
#             else:
#                 x = blk(x)
#         if self.downsample is not None:
#             x = self.downsample(x)
#         return x
#
#     def extra_repr(self) -> str:
#         return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
#
#     def flops(self):
#         flops = 0
#         for blk in self.blocks:
#             flops += blk.flops()
#         if self.downsample is not None:
#             flops += self.downsample.flops()
#         return flops


# class PatchEmbed(nn.Module):
#     r""" Image to Patch Embedding
#
#     Args:
#         img_size (int): Image size.  Default: 224.
#         patch_size (int): Patch token size. Default: 4.
#         in_chans (int): Number of input image channels. Default: 3.
#         embed_dim (int): Number of linear projection output channels. Default: 96.
#         norm_layer (nn.Module, optional): Normalization layer. Default: None
#     """
#
#     def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
#         super().__init__()
#         img_size = to_2tuple(img_size)
#         patch_size = to_2tuple(patch_size)
#         patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
#         self.img_size = img_size
#         self.patch_size = patch_size
#         self.patches_resolution = patches_resolution
#         self.num_patches = patches_resolution[0] * patches_resolution[1]
#
#         self.in_chans = in_chans
#         self.embed_dim = embed_dim
#
#         self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
#         if norm_layer is not None:
#             self.norm = norm_layer(embed_dim)
#         else:
#             self.norm = None
#
#     def forward(self, x):
#         B, C, H, W = x.shape
#         # FIXME look at relaxing size constraints
#         assert H == self.img_size[0] and W == self.img_size[1], \
#             f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
#         x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
#         if self.norm is not None:
#             x = self.norm(x)
#         return x
#
#     def flops(self):
#         Ho, Wo = self.patches_resolution
#         flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
#         if self.norm is not None:
#             flops += Ho * Wo * self.embed_dim
#         return flops


# class FLattenSwinTransformer(nn.Module):
#     r""" Swin Transformer
#         A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
#           https://arxiv.org/pdf/2103.14030
#
#     Args:
#         img_size (int | tuple(int)): Input image size. Default 224
#         patch_size (int | tuple(int)): Patch size. Default: 4
#         in_chans (int): Number of input image channels. Default: 3
#         num_classes (int): Number of classes for classification head. Default: 1000
#         embed_dim (int): Patch embedding dimension. Default: 96
#         depths (tuple(int)): Depth of each Swin Transformer layer.
#         num_heads (tuple(int)): Number of attention heads in different layers.
#         window_size (int): Window size. Default: 7
#         mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
#         qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
#         qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
#         drop_rate (float): Dropout rate. Default: 0
#         attn_drop_rate (float): Attention dropout rate. Default: 0
#         drop_path_rate (float): Stochastic depth rate. Default: 0.1
#         norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
#         ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
#         patch_norm (bool): If True, add normalization after patch embedding. Default: True
#         use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
#     """
#
#     def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
#                  embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
#                  window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
#                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
#                  norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
#                  use_checkpoint=False,
#                  focusing_factor=3, kernel_size=5, attn_type='LLLL', **kwargs):
#         super().__init__()
#
#         self.num_classes = num_classes
#         self.num_layers = len(depths)
#         self.embed_dim = embed_dim
#         self.ape = ape
#         self.patch_norm = patch_norm
#         self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
#         self.mlp_ratio = mlp_ratio
#
#         # split image into non-overlapping patches
#         self.patch_embed = PatchEmbed(
#             img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
#             norm_layer=norm_layer if self.patch_norm else None)
#         num_patches = self.patch_embed.num_patches
#         patches_resolution = self.patch_embed.patches_resolution
#         self.patches_resolution = patches_resolution
#
#         # absolute position embedding
#         if self.ape:
#             self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
#             trunc_normal_(self.absolute_pos_embed, std=.02)
#
#         self.pos_drop = nn.Dropout(p=drop_rate)
#
#         # stochastic depth
#         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
#
#         # build layers
#         self.layers = nn.ModuleList()
#         for i_layer in range(self.num_layers):
#             layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
#                                input_resolution=(patches_resolution[0] // (2 ** i_layer),
#                                                  patches_resolution[1] // (2 ** i_layer)),
#                                depth=depths[i_layer],
#                                num_heads=num_heads[i_layer],
#                                window_size=window_size,
#                                mlp_ratio=self.mlp_ratio,
#                                qkv_bias=qkv_bias, qk_scale=qk_scale,
#                                drop=drop_rate, attn_drop=attn_drop_rate,
#                                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
#                                norm_layer=norm_layer,
#                                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
#                                use_checkpoint=use_checkpoint,
#                                focusing_factor=focusing_factor,
#                                kernel_size=kernel_size,
#                                attn_type=attn_type[i_layer] + (attn_type[self.num_layers:] if attn_type[i_layer] == 'M' else ''))
#             self.layers.append(layer)
#
#         self.norm = norm_layer(self.num_features)
#         self.avgpool = nn.AdaptiveAvgPool1d(1)
#         self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
#
#         self.apply(self._init_weights)
#
#     def _init_weights(self, m):
#         if isinstance(m, nn.Linear):
#             trunc_normal_(m.weight, std=.02)
#             if isinstance(m, nn.Linear) and m.bias is not None:
#                 nn.init.constant_(m.bias, 0)
#         elif isinstance(m, nn.LayerNorm):
#             nn.init.constant_(m.bias, 0)
#             nn.init.constant_(m.weight, 1.0)
#
#     @torch.jit.ignore
#     def no_weight_decay(self):
#         return {'absolute_pos_embed'}
#
#     @torch.jit.ignore
#     def no_weight_decay_keywords(self):
#         return {'relative_position_bias_table'}
#
#     def forward_features(self, x):
#         x = self.patch_embed(x)
#         if self.ape:
#             x = x + self.absolute_pos_embed
#         x = self.pos_drop(x)
#
#         for layer in self.layers:
#             x = layer(x)
#
#         x = self.norm(x)  # B L C
#         x = self.avgpool(x.transpose(1, 2))  # B C 1
#         x = torch.flatten(x, 1)
#         return x
#
#     def forward(self, x):
#         x = self.forward_features(x)
#         x = self.head(x)
#         return x
#
#     def flops(self):
#         flops = 0
#         flops += self.patch_embed.flops()
#         for i, layer in enumerate(self.layers):
#             flops += layer.flops()
#         flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
#         flops += self.num_features * self.num_classes
#         return flops

if __name__ == '__main__':
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    input = torch.randn(1, 128, 8, 8)
    siatt = FocusedLinearAttention(dim=128).to(device)
    output = siatt(input.to(device))
    print(output.shape)
